Sensor Web Project

This project is a graphical user interface (GUI) application built using Python and customtkinter. It integrates APIs from NASA and OpenWeather to create a network of sensors capable of retrieving satellite images, weather data, and other environmental observations based on user-provided coordinates. The project also includes an anomaly detection feature using IsolationForest to analyze sensor data.

Table of Contents

• Introduction
• Objectives
• System Architecture
• Features
• Technologies Used
• Installation
• Usage
• Configuration
• Project Structure
• Results
• Limitations
• Future Enhancements
• Contributing
• License
• References

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Introduction

The Sensor Web Project is designed to simulate a network of sensors capable of real-time data acquisition and analysis. It serves as a prototype for autonomous observation systems, demonstrating how satellite imagery and weather data can be integrated for environmental monitoring.

Objectives

• Build a software system to integrate satellite and weather data from external APIs.
• Visualize sensor data and detect anomalies using machine learning techniques.
• Provide an intuitive graphical user interface for interaction.

System Architecture

The system includes:

1. Data Acquisition: Uses NASA Earth Assets API for satellite images and OpenWeather API for weather data.
2. Data Processing: Generates synthetic sensor readings and detects anomalies using the IsolationForest algorithm.
3. Visualization: Displays sensor data and satellite images using matplotlib and PIL.

Features

• Sensor Data Simulation: Generates synthetic temperature data using numpy.
• Anomaly Detection: Detects anomalies using IsolationForest from scikit-learn.
• Satellite Imagery: Retrieves images from the NASA Earth Assets API based on user coordinates.
• Weather Data: Fetches current weather conditions using OpenWeather API.
• Interactive GUI: Allows users to input coordinates and view real-time results.

Technologies Used

• Python
• customtkinter for the GUI
• requests for API calls
• scikit-learn for anomaly detection
• matplotlib for plotting graphs
• PIL (Pillow) for image handling
• NASA Earth Assets API
• OpenWeather API

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/sensor-web-project.git
   cd sensor-web-project
   

usage

• Enter Latitude and Longitude: The application fetches satellite images and weather data for the specified coordinates.
• Generate Sensor Data: Simulates a set of sensor readings.
• Anomaly Detection: Analyzes the generated sensor data and identifies anomalies.

Configuration

• Ensure your environment is properly set up with the required API keys.

• Adjust parameters like sensor data count or plotting dimensions if needed.

• Project Structure -sensor-web-project

• main.py Main entry point of the application.

• requirements.txt List of dependencies.

• README.md Project documentation.

Results

• The project demonstrates successful integration of real-world data from external APIs with simulated sensor data. It provides:

• Real-time visualization of satellite images based on user inputs.

• Accurate weather data retrieval for any location worldwide.

• Detection of outliers in sensor data, highlighting potential anomalies.

Limitations

• Requires API keys with potential usage limits.
• Relies on internet connectivity for data fetching.
• Synthetic sensor data may not fully represent real-world conditions.

Future Enhancements

• Integrate more advanced machine learning models for anomaly detection.
• Expand the sensor network to include additional environmental data like air quality.
• Implement a database for historical data storage. -Develop a web-based version for broader accessibility.

Contributing

Feel free to submit issues, fork the repository, and create pull requests.

License

This project is licensed under the MIT License.

References

• NASA Earth Assets API: 'https://api.nasa.gov'.
• OpenWeather API: 'https://openweathermap.org/api'.
• scikit-learn Documentation: 'https://scikit-learn.org'.